


from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerModel,
    )
    from transformers.models.roformer.modeling_tf_roformer import (
        TFRoFormerSelfAttention,
        TFRoFormerSinusoidalPositionalEmbedding,
    )


class  __SCREAMING_SNAKE_CASE       :


     def __init__(			self      :		List[str]       ,  __UpperCamelCase      :		List[str]       ,  __UpperCamelCase      :		str=13       ,  __UpperCamelCase      :		Tuple=7       ,  __UpperCamelCase      :		Any=True       ,  __UpperCamelCase      :		str=True       ,  __UpperCamelCase      :		List[Any]=True       ,  __UpperCamelCase      :		List[Any]=True       ,  __UpperCamelCase      :		Optional[Any]=99       ,  __UpperCamelCase      :		Dict=32       ,  __UpperCamelCase      :		Dict=2       ,  __UpperCamelCase      :		List[Any]=4       ,  __UpperCamelCase      :		List[Any]=37       ,  __UpperCamelCase      :		Any="gelu"       ,  __UpperCamelCase      :		Dict=0.1       ,  __UpperCamelCase      :		List[Any]=0.1       ,  __UpperCamelCase      :		int=512       ,  __UpperCamelCase      :		Dict=16       ,  __UpperCamelCase      :		int=2       ,  __UpperCamelCase      :		Union[str, Any]=0.02       ,  __UpperCamelCase      :		str=3       ,  __UpperCamelCase      :		Optional[Any]=4       ,  __UpperCamelCase      :		List[str]=None       ,  ):
       _UpperCAmelCase												=     parent
       _UpperCAmelCase												=     13
       _UpperCAmelCase												=     7
       _UpperCAmelCase												=     True
       _UpperCAmelCase												=     True
       _UpperCAmelCase												=     True
       _UpperCAmelCase												=     True
       _UpperCAmelCase												=     99
       _UpperCAmelCase												=     32
       _UpperCAmelCase												=     2
       _UpperCAmelCase												=     4
       _UpperCAmelCase												=     37
       _UpperCAmelCase												=     "gelu"
       _UpperCAmelCase												=     0.1
       _UpperCAmelCase												=     0.1
       _UpperCAmelCase												=     512
       _UpperCAmelCase												=     16
       _UpperCAmelCase												=     2
       _UpperCAmelCase												=     0.02
       _UpperCAmelCase												=     3
       _UpperCAmelCase												=     4
       _UpperCAmelCase												=     None


     def 							UpperCAmelCase__  (			self      :		Any ):
       _UpperCAmelCase												=     ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size )

       _UpperCAmelCase												=     None
       if self.use_input_mask:
         _UpperCAmelCase												=     random_attention_mask([self.batch_size, self.seq_length] )

       _UpperCAmelCase												=     None
       if self.use_token_type_ids:
         _UpperCAmelCase												=     ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size )

       _UpperCAmelCase												=     None
       _UpperCAmelCase												=     None
       _UpperCAmelCase												=     None
       if self.use_labels:
         _UpperCAmelCase												=     ids_tensor([self.batch_size]       ,  self.type_sequence_label_size )
         _UpperCAmelCase												=     ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels )
         _UpperCAmelCase												=     ids_tensor([self.batch_size]       ,  self.num_choices )

       _UpperCAmelCase												=     RoFormerConfig(
           vocab_size=self.vocab_size       ,  hidden_size=self.hidden_size       ,  num_hidden_layers=self.num_hidden_layers       ,  num_attention_heads=self.num_attention_heads       ,  intermediate_size=self.intermediate_size       ,  hidden_act=self.hidden_act       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  max_position_embeddings=self.max_position_embeddings       ,  type_vocab_size=self.type_vocab_size       ,  initializer_range=self.initializer_range       ,  return_dict=__UpperCamelCase       ,  )

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


     def 							UpperCAmelCase__  (			self      :		Union[str, Any]       ,  __UpperCamelCase      :		List[Any]       ,  __UpperCamelCase      :		Dict       ,  __UpperCamelCase      :		Optional[int]       ,  __UpperCamelCase      :		List[Any]       ,  __UpperCamelCase      :		Any       ,  __UpperCamelCase      :		str       ,  __UpperCamelCase      :		List[Any] ):
       _UpperCAmelCase												=     TFRoFormerModel(config=__UpperCamelCase )
       _UpperCAmelCase												=     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

       _UpperCAmelCase												=     [input_ids, input_mask]
       _UpperCAmelCase												=     model(__UpperCamelCase )

       _UpperCAmelCase												=     model(__UpperCamelCase )

       self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size) )


     def 							UpperCAmelCase__  (			self      :		Union[str, Any]       ,  __UpperCamelCase      :		int       ,  __UpperCamelCase      :		Union[str, Any]       ,  __UpperCamelCase      :		Union[str, Any]       ,  __UpperCamelCase      :		str       ,  __UpperCamelCase      :		Optional[Any]       ,  __UpperCamelCase      :		Any       ,  __UpperCamelCase      :		str ):
       _UpperCAmelCase												=     True
       _UpperCAmelCase												=     TFRoFormerForCausalLM(config=__UpperCamelCase )
       _UpperCAmelCase												=     {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       _UpperCAmelCase												=     model(__UpperCamelCase )["logits"]
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape )       ,  [self.batch_size, self.seq_length, self.vocab_size] )


     def 							UpperCAmelCase__  (			self      :		Union[str, Any]       ,  __UpperCamelCase      :		Optional[Any]       ,  __UpperCamelCase      :		Optional[int]       ,  __UpperCamelCase      :		int       ,  __UpperCamelCase      :		Optional[int]       ,  __UpperCamelCase      :		int       ,  __UpperCamelCase      :		Dict       ,  __UpperCamelCase      :		Any ):
       _UpperCAmelCase												=     TFRoFormerForMaskedLM(config=__UpperCamelCase )
       _UpperCAmelCase												=     {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       _UpperCAmelCase												=     model(__UpperCamelCase )
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size) )


     def 							UpperCAmelCase__  (			self      :		Union[str, Any]       ,  __UpperCamelCase      :		Optional[int]       ,  __UpperCamelCase      :		Optional[int]       ,  __UpperCamelCase      :		str       ,  __UpperCamelCase      :		Dict       ,  __UpperCamelCase      :		List[Any]       ,  __UpperCamelCase      :		Tuple       ,  __UpperCamelCase      :		int ):
       _UpperCAmelCase												=     self.num_labels
       _UpperCAmelCase												=     TFRoFormerForSequenceClassification(config=__UpperCamelCase )
       _UpperCAmelCase												=     {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       _UpperCAmelCase												=     model(__UpperCamelCase )
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels) )


     def 							UpperCAmelCase__  (			self      :		str       ,  __UpperCamelCase      :		Union[str, Any]       ,  __UpperCamelCase      :		Optional[int]       ,  __UpperCamelCase      :		str       ,  __UpperCamelCase      :		Dict       ,  __UpperCamelCase      :		Optional[Any]       ,  __UpperCamelCase      :		List[Any]       ,  __UpperCamelCase      :		List[str] ):
       _UpperCAmelCase												=     self.num_choices
       _UpperCAmelCase												=     TFRoFormerForMultipleChoice(config=__UpperCamelCase )
       _UpperCAmelCase												=     tf.tile(tf.expand_dims(__UpperCamelCase       ,  1 )       ,  (1, self.num_choices, 1) )
       _UpperCAmelCase												=     tf.tile(tf.expand_dims(__UpperCamelCase       ,  1 )       ,  (1, self.num_choices, 1) )
       _UpperCAmelCase												=     tf.tile(tf.expand_dims(__UpperCamelCase       ,  1 )       ,  (1, self.num_choices, 1) )
       _UpperCAmelCase												=     {
           "input_ids": multiple_choice_inputs_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       _UpperCAmelCase												=     model(__UpperCamelCase )
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_choices) )


     def 							UpperCAmelCase__  (			self      :		List[str]       ,  __UpperCamelCase      :		Dict       ,  __UpperCamelCase      :		int       ,  __UpperCamelCase      :		Any       ,  __UpperCamelCase      :		List[str]       ,  __UpperCamelCase      :		Tuple       ,  __UpperCamelCase      :		str       ,  __UpperCamelCase      :		List[Any] ):
       _UpperCAmelCase												=     self.num_labels
       _UpperCAmelCase												=     TFRoFormerForTokenClassification(config=__UpperCamelCase )
       _UpperCAmelCase												=     {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       _UpperCAmelCase												=     model(__UpperCamelCase )
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.num_labels) )


     def 							UpperCAmelCase__  (			self      :		Optional[Any]       ,  __UpperCamelCase      :		Tuple       ,  __UpperCamelCase      :		Dict       ,  __UpperCamelCase      :		Union[str, Any]       ,  __UpperCamelCase      :		Union[str, Any]       ,  __UpperCamelCase      :		List[Any]       ,  __UpperCamelCase      :		List[Any]       ,  __UpperCamelCase      :		Optional[int] ):
       _UpperCAmelCase												=     TFRoFormerForQuestionAnswering(config=__UpperCamelCase )
       _UpperCAmelCase												=     {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       _UpperCAmelCase												=     model(__UpperCamelCase )
       self.parent.assertEqual(result.start_logits.shape       ,  (self.batch_size, self.seq_length) )
       self.parent.assertEqual(result.end_logits.shape       ,  (self.batch_size, self.seq_length) )



     def 							UpperCAmelCase__  (			self      :		Optional[int] ):
       _UpperCAmelCase												=     self.prepare_config_and_inputs()
       (
           (
           _UpperCAmelCase						
       )						,		(
           _UpperCAmelCase						
       )						,		(
           _UpperCAmelCase						
       )						,		(
           _UpperCAmelCase						
       )						,		(
           _UpperCAmelCase						
       )						,		(
           _UpperCAmelCase						
       )						,		(
           _UpperCAmelCase						
       )						,								
       )												=     config_and_inputs
       _UpperCAmelCase												=     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict





@require_tf
class  __SCREAMING_SNAKE_CASE       (						_UpperCAmelCase  ,     _UpperCAmelCase  ,     unittest.TestCase):
     __SCREAMING_SNAKE_CASE  : List[str]     =			(
         (
             TFRoFormerModel,
             TFRoFormerForCausalLM,
             TFRoFormerForMaskedLM,
             TFRoFormerForQuestionAnswering,
             TFRoFormerForSequenceClassification,
             TFRoFormerForTokenClassification,
             TFRoFormerForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     __SCREAMING_SNAKE_CASE  : int     =			(
         {
             """feature-extraction""": TFRoFormerModel,
             """fill-mask""": TFRoFormerForMaskedLM,
             """question-answering""": TFRoFormerForQuestionAnswering,
             """text-classification""": TFRoFormerForSequenceClassification,
             """text-generation""": TFRoFormerForCausalLM,
             """token-classification""": TFRoFormerForTokenClassification,
             """zero-shot""": TFRoFormerForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     __SCREAMING_SNAKE_CASE  : List[Any]     =			False
     __SCREAMING_SNAKE_CASE  : Union[str, Any]     =			False


     def 							UpperCAmelCase__  (			self      :		Tuple       ,  __UpperCamelCase      :		Dict       ,  __UpperCamelCase      :		List[str]       ,  __UpperCamelCase      :		List[str]       ,  __UpperCamelCase      :		Optional[int]       ,  __UpperCamelCase      :		str ):
       if pipeline_test_casse_name == "TextGenerationPipelineTests":
         return True

       return False


     def 							UpperCAmelCase__  (			self      :		Tuple ):
       _UpperCAmelCase												=     TFRoFormerModelTester(self )
       _UpperCAmelCase												=     ConfigTester(self       ,  config_class=__UpperCamelCase       ,  hidden_size=37 )


     def 							UpperCAmelCase__  (			self      :		Any ):
       self.config_tester.run_common_tests()


     def 							UpperCAmelCase__  (			self      :		Tuple ):
       _UpperCAmelCase												=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCamelCase )


     def 							UpperCAmelCase__  (			self      :		Union[str, Any] ):
       _UpperCAmelCase												=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )


     def 							UpperCAmelCase__  (			self      :		Tuple ):
       _UpperCAmelCase												=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*__UpperCamelCase )


     def 							UpperCAmelCase__  (			self      :		Optional[int] ):
       _UpperCAmelCase												=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase )


     def 							UpperCAmelCase__  (			self      :		List[Any] ):
       _UpperCAmelCase												=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )


     def 							UpperCAmelCase__  (			self      :		Optional[int] ):
       _UpperCAmelCase												=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )


     def 							UpperCAmelCase__  (			self      :		str ):
       _UpperCAmelCase												=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )



     @slow
     def 							UpperCAmelCase__  (			self      :		int ):
       _UpperCAmelCase												=     TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base" )
       self.assertIsNotNone(__UpperCamelCase )





@require_tf
class  __SCREAMING_SNAKE_CASE       (						unittest.TestCase):


     @slow
     def 							UpperCAmelCase__  (			self      :		List[str] ):
       _UpperCAmelCase												=     TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
       _UpperCAmelCase												=     tf.constant([[0, 1, 2, 3, 4, 5]] )
       _UpperCAmelCase												=     model(__UpperCamelCase )[0]

       # TODO Replace vocab size
       _UpperCAmelCase												=     50_000

       _UpperCAmelCase												=     [1, 6, vocab_size]
       self.assertEqual(output.shape       ,  __UpperCamelCase )

       print(output[:, :3, :3] )

       # TODO Replace values below with what was printed above.
       _UpperCAmelCase												=     tf.constant(
           [
               [
                   [-0.12053341, -1.0264901, 0.29221946],
                   [-1.5133783, 0.197433, 0.15190607],
                   [-5.0135403, -3.900256, -0.84038764],
               ]
           ] )
       tf.debugging.assert_near(output[:, :3, :3]       ,  __UpperCamelCase       ,  atol=1e-4 )





@require_tf
class  __SCREAMING_SNAKE_CASE       (						unittest.TestCase):
     __SCREAMING_SNAKE_CASE  : List[str]     =			1e-4


     def 							UpperCAmelCase__  (			self      :		Any ):
       _UpperCAmelCase												=     tf.constant([[4, 10]] )
       _UpperCAmelCase												=     TFRoFormerSinusoidalPositionalEmbedding(num_positions=6       ,  embedding_dim=6 )

       _UpperCAmelCase												=     emba(input_ids.shape )
       _UpperCAmelCase												=     tf.constant(
           [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )

       tf.debugging.assert_near(__UpperCamelCase       ,  __UpperCamelCase       ,  atol=self.tolerance )



     def 							UpperCAmelCase__  (			self      :		str ):
       _UpperCAmelCase												=     tf.constant(
           [
               [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
               [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
               [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
           ] )
       _UpperCAmelCase												=     TFRoFormerSinusoidalPositionalEmbedding(num_positions=512       ,  embedding_dim=512 )
       emba([2, 16, 512] )
       _UpperCAmelCase												=     emba.weight[:3, :5]

       tf.debugging.assert_near(__UpperCamelCase       ,  __UpperCamelCase       ,  atol=self.tolerance )





@require_tf
class  __SCREAMING_SNAKE_CASE       (						unittest.TestCase):
     __SCREAMING_SNAKE_CASE  : List[str]     =			1e-4


     def 							UpperCAmelCase__  (			self      :		Any ):
       # 2,12,16,64
       _UpperCAmelCase												=     tf.reshape(tf.range(2 * 12 * 16 * 64       ,  dtype=tf.floataa )       ,  shape=(2, 12, 16, 64) ) / 100

       _UpperCAmelCase												=     -tf.reshape(tf.range(2 * 12 * 16 * 64       ,  dtype=tf.floataa )       ,  shape=(2, 12, 16, 64) ) / 100

       _UpperCAmelCase												=     TFRoFormerSinusoidalPositionalEmbedding(num_positions=32       ,  embedding_dim=64 )
       _UpperCAmelCase												=     embed_positions([2, 16, 768] )[None, None, :, :]

       _UpperCAmelCase						,		_UpperCAmelCase												=     TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           __UpperCamelCase       ,  __UpperCamelCase       ,  __UpperCamelCase )

       _UpperCAmelCase												=     tf.constant(
           [
               [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
               [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
               [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
               [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
               [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
               [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
           ] )
       _UpperCAmelCase												=     tf.constant(
           [
               [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
               [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
               [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
               [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
               [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
               [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
           ] )

       tf.debugging.assert_near(query_layer[0, 0, :6, :8]       ,  __UpperCamelCase       ,  atol=self.tolerance )
       tf.debugging.assert_near(key_layer[0, 0, :6, :8]       ,  __UpperCamelCase       ,  atol=self.tolerance )










"""simple docstring"""





import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
       from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor

class 						lowercase   :





      def __init__(			self ,		lowercase ,		lowercase=13 ,		lowercase=32 ,		lowercase=2 ,		lowercase=3 ,		lowercase=16 ,		lowercase=[1, 2, 1] ,		lowercase=[2, 2, 4] ,		lowercase=2 ,		lowercase=2.0 ,		lowercase=True ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.1 ,		lowercase="gelu" ,		lowercase=False ,		lowercase=True ,		lowercase=0.02 ,		lowercase=1e-5 ,		lowercase=True ,		lowercase=None ,		lowercase=True ,		lowercase=10 ,		lowercase=8 ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			parent
            lowerCAmelCase    		=			batch_size
            lowerCAmelCase    		=			image_size
            lowerCAmelCase    		=			patch_size
            lowerCAmelCase    		=			num_channels
            lowerCAmelCase    		=			embed_dim
            lowerCAmelCase    		=			depths
            lowerCAmelCase    		=			num_heads
            lowerCAmelCase    		=			window_size
            lowerCAmelCase    		=			mlp_ratio
            lowerCAmelCase    		=			qkv_bias
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			attention_probs_dropout_prob
            lowerCAmelCase    		=			drop_path_rate
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			use_absolute_embeddings
            lowerCAmelCase    		=			patch_norm
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			is_training
            lowerCAmelCase    		=			scope
            lowerCAmelCase    		=			use_labels
            lowerCAmelCase    		=			type_sequence_label_size
            lowerCAmelCase    		=			encoder_stride





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

            lowerCAmelCase    		=			None
            if self.use_labels:
                  lowerCAmelCase    		=			ids_tensor([self.batch_size] ,		self.type_sequence_label_size					)

            lowerCAmelCase    		=			self.get_config()

            return config, pixel_values, labels





      def        _snake_case     (			self					)						->	str:
            return SwinvaConfig(
                image_size=self.image_size ,		patch_size=self.patch_size ,		num_channels=self.num_channels ,		embed_dim=self.embed_dim ,		depths=self.depths ,		num_heads=self.num_heads ,		window_size=self.window_size ,		mlp_ratio=self.mlp_ratio ,		qkv_bias=self.qkv_bias ,		hidden_dropout_prob=self.hidden_dropout_prob ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob ,		drop_path_rate=self.drop_path_rate ,		hidden_act=self.hidden_act ,		use_absolute_embeddings=self.use_absolute_embeddings ,		path_norm=self.patch_norm ,		layer_norm_eps=self.layer_norm_eps ,		initializer_range=self.initializer_range ,		encoder_stride=self.encoder_stride ,		)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	Tuple:
            lowerCAmelCase    		=			SwinvaModel(config=lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase					)

            lowerCAmelCase    		=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
            lowerCAmelCase    		=			int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

            self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, expected_seq_len, expected_dim)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	str:
            lowerCAmelCase    		=			SwinvaForMaskedImageModeling(config=lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(
                result.logits.shape ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

            # test greyscale images
            lowerCAmelCase    		=			1
            lowerCAmelCase    		=			SwinvaForMaskedImageModeling(lowercase					)
            model.to(lowercase					)
            model.eval()

            lowerCAmelCase    		=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, 1, self.image_size, self.image_size)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			self.type_sequence_label_size
            lowerCAmelCase    		=			SwinvaForImageClassification(lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase ,		labels=lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.prepare_config_and_inputs()
            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			config_and_inputs
            lowerCAmelCase    		=			{"""pixel_values""": pixel_values}
            return config, inputs_dict







@require_torch
class 						lowercase   (   _UpperCAmelCase	,    _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = (
          (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
      )
      _SCREAMING_SNAKE_CASE							      = (
          {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
          if is_torch_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			SwinvaModelTester(self					)
            lowerCAmelCase    		=			ConfigTester(self ,		config_class=lowercase ,		embed_dim=37					)





      def        _snake_case     (			self					)						->	Tuple:
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase					)





      @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""					)
      def        _snake_case     (			self					)						->	str:
            pass





      @unittest.skip(reason="""Swinv2 does not use inputs_embeds"""					)
      def        _snake_case     (			self					)						->	str:
            pass





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(lowercase					)
                  self.assertIsInstance(model.get_input_embeddings() ,		(nn.Module)					)
                  lowerCAmelCase    		=			model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(lowercase ,		nn.Linear					)					)





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(lowercase					)
                  lowerCAmelCase    		=			inspect.signature(model.forward					)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowerCAmelCase    		=			[*signature.parameters.keys()]

                  lowerCAmelCase    		=			["""pixel_values"""]
                  self.assertListEqual(arg_names[:1] ,		lowercase					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()
            lowerCAmelCase    		=			True

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			False
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)
                  lowerCAmelCase    		=			outputs.attentions
                  lowerCAmelCase    		=			len(self.model_tester.depths					)
                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			config.window_size**2
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)
                  lowerCAmelCase    		=			outputs.attentions
                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  self.assertListEqual(
                      list(attentions[0].shape[-3:]					) ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,		)
                  lowerCAmelCase    		=			len(lowercase					)

                  # Check attention is always last and order is fine
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)

                  if hasattr(self.model_tester ,		"""num_hidden_states_types"""					):
                        lowerCAmelCase    		=			self.model_tester.num_hidden_states_types
                  else:
                        # also another +1 for reshaped_hidden_states
                        lowerCAmelCase    		=			2
                  self.assertEqual(out_len + added_hidden_states ,		len(lowercase					)					)

                  lowerCAmelCase    		=			outputs.attentions

                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  self.assertListEqual(
                      list(self_attentions[0].shape[-3:]					) ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,		)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	List[str]:
            lowerCAmelCase    		=			model_class(lowercase					)
            model.to(lowercase					)
            model.eval()

            with torch.no_grad():
                  lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)

            lowerCAmelCase    		=			outputs.hidden_states

            lowerCAmelCase    		=			getattr(
                self.model_tester ,		"""expected_num_hidden_layers""" ,		len(self.model_tester.depths					) + 1					)
            self.assertEqual(len(lowercase					) ,		lowercase					)

            # Swinv2 has a different seq_length
            lowerCAmelCase    		=			(
                config.patch_size
                if isinstance(config.patch_size ,		collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            lowerCAmelCase    		=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]					) ,		[num_patches, self.model_tester.embed_dim] ,		)

            lowerCAmelCase    		=			outputs.reshaped_hidden_states
            self.assertEqual(len(lowercase					) ,		lowercase					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			reshaped_hidden_states[0].shape
            lowerCAmelCase    		=			(
                reshaped_hidden_states[0].view(lowercase ,		lowercase ,		height * width					).permute(0 ,		2 ,		1					)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]					) ,		[num_patches, self.model_tester.embed_dim] ,		)





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            lowerCAmelCase    		=			(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size ,		collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		lowercase					)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowerCAmelCase    		=			True

                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		lowercase					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()
            lowerCAmelCase    		=			3

            lowerCAmelCase    		=			(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size ,		collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )
            lowerCAmelCase    		=			(
                config.patch_size
                if isinstance(config.patch_size ,		collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            lowerCAmelCase    		=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            lowerCAmelCase    		=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		(padded_height, padded_width)					)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		(padded_height, padded_width)					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*lowercase					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowercase					)





      @slow
      def        _snake_case     (			self					)						->	Any:
            for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase    		=			SwinvaModel.from_pretrained(lowercase					)
                  self.assertIsNotNone(lowercase					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            lowerCAmelCase    		=			_config_zero_init(lowercase					)
            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(config=lowercase					)
                  for name, param in model.named_parameters():
                        if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                              self.assertIn(
                                  ((param.data.mean() * 1e9).round() / 1e9).item() ,		[0.0, 1.0] ,		msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,		)







@require_vision
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      @cached_property
      def        _snake_case     (			self					)						->	Any:
            return (
                AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					)
                if is_vision_available()
                else None
            )





      @slow
      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					).to(
                lowercase					)
            lowerCAmelCase    		=			self.default_image_processor

            lowerCAmelCase    		=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
            lowerCAmelCase    		=			image_processor(images=lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)

            # verify the logits
            lowerCAmelCase    		=			torch.Size((1, 1_000)					)
            self.assertEqual(outputs.logits.shape ,		lowercase					)
            lowerCAmelCase    		=			torch.tensor([-0.3_947, -0.4_306, 0.0_026]					).to(lowercase					)
            self.assertTrue(torch.allclose(outputs.logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






